In Section §REF , we described a task-oriented system as a pipeline of components, saying that SF and IC are core tasks at the NLU level. Particularly, IC consists of classifying an utterance with a set of pre-defined intents, while SF is defined as a sequence tagging problem [1]}, [2]}, where each token of the utterance has to be tagged with a slot label. In this scenario training data for SF typically consist of single utterances in a dialogue where tokens are annotated with a pre-defined set of slot names, and slot values correspond to arbitrary sequences of tokens. In this perspective, it is worth mentioning a research line on dialogue state tracking [3]}, [4]}, [5]}, where the NLU component is usually embedded into DST. What is relevant for our topic is that in this context SF is defined as a classification problem: given the current utterance and the previous dialogue history, the system has to decide whether a certain slot-value pair defined in the domain ontology is referred or not in the current utterance. Although promising, from the NLU perspective, this research line poses constraints (e.g. all slot-value pairs have to be pre-defined in an ontology,) that limit the SF applicability. For this reason, and because NLU components are the prevalent solution in current task-oriented systems, the focus of our survey will be on SF as a sequence tagging problem, as more precisely defined in the next section.
